ML-Gann Hybrid Trend Integrator
Family: trend_following · Regime: trending · Complexity: high · Asset classes: FX, Indices, Crypto · Timeframes: H1, H4, D1
Thesis
This strategy operates on the hypothesis that price trends validated by a Deep Q-Learning model (ML SuperTrend) are more likely to persist than those identified by static linear indicators. By using an LSTM to dynamically adjust trend-detection sensitivity based on local volatility, the system identifies higher-probability entries. To prevent the 'black box' failure of ML, the strategy relies on a transparent, structural Gann HiLo exit, ensuring that the trade is closed based on objective price-action shifts rather than neural network outputs alone. The Bar Counter serves as a prerequisite for data sufficiency.
Components
- MQL4 Indicator Sample Bar Counter (regime) — Acts as a 'Data Integrity' regime filter, ensuring the chart has sufficient historical depth (e.g., >500 bars) for the LSTM/ML model to have a populated lookback buffer before signals are considered valid.
- ML SuperTrend (Ultimate) - Auto-Optimized AI with LSTM (direction) — Provides the primary trend direction by dynamically adjusting the ATR multiplier using a deep learning architecture to adapt to shifting volatility regimes.
- Arrows Indicator Template (entry) — Serves as the signal execution layer, triggering an arrow when the price closes on the signal side of the ML SuperTrend, provided the regime filter is met.
- HiLo_04 (Gann HiLo Activator) (exit) — Used as a structural trend-stop; once price closes on the opposite side of the High/Low SMA channel, the trend is considered exhausted.
- Average True Range (ATR) (risk) — Standardizes risk by setting volatility-based stop-loss distances and calculating position size.
Known failure conditions
- Strategy produces entry signals when the Bar Counter indicates low data availability (<200 bars).
- ML SuperTrend 'base_fact' remains static across wildly different volatility regimes (indicating AI failure).
- The Gann HiLo exit triggers consistently before price reaches the 1.0 ATR profit level, indicating the exit is too tight for the ML-defined trend.
Explore the full interactive blueprint with parameter ranges and evidence on WOBR StrategyVerse, or generate this strategy as an MT4/MT5 Expert Advisor with QuantMogul AI Engine (free download).